debug

eval_mixed_model.py

@@ -39,9 +39,11 @@ parser.add_argument("alpha",type=float)
 parser.add_argument("nb_iterations",type=int)
 parser.add_argument('-f', '--features', help='Feature(s) used in the model training', type=str)
 parser.add_argument("-v","--verbose",action="store_true")
+parser.add_argument("-t","--timeout",default=30,type=int)
 
 args= parser.parse_args()
 
+# COMMAND LINE ARGS VALUES
 GRAPH_NODE_NB = args.nb_nodes
 GRAPH_EDGE_NB = args.nb_edges
 ALPHA = args.alpha
@@ -49,7 +51,8 @@ NB_COM = args.nb_com
 NB_ITERATION = args.nb_iterations
 VERBOSE = args.verbose
 FEATURES = set(args.features.split(","))
-TIMEOUT = 60
+TIMEOUT = args.timeout
+
 dist = lambda a,b : np.linalg.norm(a-b)**2
 hash_func = lambda x:"_".join(sorted([str(x[0]),str(x[1])]))
 
@@ -63,7 +66,7 @@ def get_aucs(G):
         auc_spatial = nee.evaluate_baseline(method="spatial_link_prediction",timeout=TIMEOUT).test_scores.auroc()
         auc_sbm = nee.evaluate_baseline(method="stochastic_block_model",timeout=TIMEOUT).test_scores.auroc()
     except:
-        print("Could not compuyte AUC ! ")
+        print("Could not compute AUC ! ")
     return auc_sbm,auc_spatial
 
 dist = lambda a,b : np.linalg.norm(a-b)